Overview

Dataset statistics

Number of variables22
Number of observations5269
Missing cells51983
Missing cells (%)44.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory905.7 KiB
Average record size in memory176.0 B

Variable types

Text10
Categorical6
Numeric4
Boolean1
DateTime1

Alerts

n_subscribers is highly overall correlated with n_reviewsHigh correlation
price is highly overall correlated with mooc and 1 other fieldsHigh correlation
n_reviews is highly overall correlated with n_subscribers and 1 other fieldsHigh correlation
n_lectures is highly overall correlated with moocHigh correlation
mooc is highly overall correlated with price and 8 other fieldsHigh correlation
modality is highly overall correlated with mooc and 1 other fieldsHigh correlation
level is highly overall correlated with mooc and 1 other fieldsHigh correlation
subject is highly overall correlated with mooc and 1 other fieldsHigh correlation
language is highly overall correlated with mooc and 2 other fieldsHigh correlation
subtitles is highly overall correlated with mooc and 2 other fieldsHigh correlation
paid is highly overall correlated with price and 6 other fieldsHigh correlation
modality is highly imbalanced (67.4%)Imbalance
language is highly imbalanced (72.0%)Imbalance
subtitles is highly imbalanced (70.1%)Imbalance
institution has 3672 (69.7%) missing valuesMissing
id has 974 (18.5%) missing valuesMissing
summary has 4348 (82.5%) missing valuesMissing
n_subscribers has 743 (14.1%) missing valuesMissing
modality has 4295 (81.5%) missing valuesMissing
instructors has 4298 (81.6%) missing valuesMissing
level has 623 (11.8%) missing valuesMissing
subject has 623 (11.8%) missing valuesMissing
language has 4295 (81.5%) missing valuesMissing
subtitles has 4298 (81.6%) missing valuesMissing
effort has 4295 (81.5%) missing valuesMissing
duration has 623 (11.8%) missing valuesMissing
price has 4295 (81.5%) missing valuesMissing
description has 4335 (82.3%) missing valuesMissing
curriculum has 4852 (92.1%) missing valuesMissing
paid has 623 (11.8%) missing valuesMissing
n_reviews has 1597 (30.3%) missing valuesMissing
n_lectures has 1597 (30.3%) missing valuesMissing
published has 1597 (30.3%) missing valuesMissing
n_subscribers is highly skewed (γ1 = 23.31018807)Skewed
url has unique valuesUnique
n_subscribers has 65 (1.2%) zerosZeros
n_reviews has 284 (5.4%) zerosZeros

Reproduction

Analysis started2023-06-15 11:20:57.559410
Analysis finished2023-06-15 11:21:21.371152
Duration23.81 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

title
Text

Distinct5240
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:21.925687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length243
Median length91
Mean length43.781173
Min length6

Characters and Unicode

Total characters230683
Distinct characters528
Distinct categories18 ?
Distinct scripts10 ?
Distinct blocks15 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5215 ?
Unique (%)99.0%

Sample

1st rowMachine Learning
2nd rowIndigenous Canada
3rd rowThe Science of Well-Being
4th rowTechnical Support Fundamentals
5th rowBecome a CBRS Certified Professional Installer by Google
ValueCountFrequency (%)
1228
 
3.5%
to 1027
 
2.9%
and 874
 
2.5%
for 739
 
2.1%
the 722
 
2.1%
a 571
 
1.6%
learn 511
 
1.5%
in 504
 
1.4%
with 451
 
1.3%
trading 316
 
0.9%
Other values (5552) 28076
80.2%
2023-06-15T08:21:23.069906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29897
 
13.0%
e 18578
 
8.1%
n 15249
 
6.6%
a 14385
 
6.2%
o 14190
 
6.2%
i 14118
 
6.1%
t 13094
 
5.7%
r 12771
 
5.5%
s 10709
 
4.6%
l 6687
 
2.9%
Other values (518) 81005
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164261
71.2%
Space Separator 29916
 
13.0%
Uppercase Letter 28722
 
12.5%
Other Punctuation 3003
 
1.3%
Decimal Number 1971
 
0.9%
Other Letter 1154
 
0.5%
Dash Punctuation 1054
 
0.5%
Close Punctuation 197
 
0.1%
Open Punctuation 196
 
0.1%
Math Symbol 101
 
< 0.1%
Other values (8) 108
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ل 52
 
4.5%
ا 46
 
4.0%
و 24
 
2.1%
22
 
1.9%
ي 22
 
1.9%
م 20
 
1.7%
ر 19
 
1.6%
19
 
1.6%
19
 
1.6%
18
 
1.6%
Other values (330) 893
77.4%
Lowercase Letter
ValueCountFrequency (%)
e 18578
11.3%
n 15249
 
9.3%
a 14385
 
8.8%
o 14190
 
8.6%
i 14118
 
8.6%
t 13094
 
8.0%
r 12771
 
7.8%
s 10709
 
6.5%
l 6687
 
4.1%
c 6233
 
3.8%
Other values (61) 38247
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 2694
 
9.4%
C 2527
 
8.8%
P 2408
 
8.4%
T 2147
 
7.5%
A 2055
 
7.2%
B 1779
 
6.2%
M 1615
 
5.6%
L 1557
 
5.4%
F 1535
 
5.3%
I 1473
 
5.1%
Other values (29) 8932
31.1%
Other Punctuation
ValueCountFrequency (%)
: 1348
44.9%
, 449
 
15.0%
& 312
 
10.4%
! 273
 
9.1%
. 242
 
8.1%
' 150
 
5.0%
/ 114
 
3.8%
# 34
 
1.1%
" 24
 
0.8%
? 19
 
0.6%
Other values (8) 38
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 496
25.2%
2 337
17.1%
0 322
16.3%
3 207
10.5%
5 204
10.4%
4 140
 
7.1%
7 85
 
4.3%
6 77
 
3.9%
8 58
 
2.9%
9 40
 
2.0%
Other values (3) 5
 
0.3%
Nonspacing Mark
ValueCountFrequency (%)
5
27.8%
2
 
11.1%
2
 
11.1%
ّ 2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Math Symbol
ValueCountFrequency (%)
+ 49
48.5%
| 44
43.6%
> 4
 
4.0%
1
 
1.0%
= 1
 
1.0%
1
 
1.0%
1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 185
93.9%
3
 
1.5%
3
 
1.5%
3
 
1.5%
] 2
 
1.0%
} 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 184
93.9%
3
 
1.5%
3
 
1.5%
3
 
1.5%
[ 2
 
1.0%
{ 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 1021
96.9%
30
 
2.8%
2
 
0.2%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
29897
99.9%
  12
 
< 0.1%
  7
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 17
85.0%
2
 
10.0%
1
 
5.0%
Letter Number
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
50.0%
´ 1
50.0%
Final Punctuation
ValueCountFrequency (%)
25
100.0%
Modifier Letter
ValueCountFrequency (%)
23
100.0%
Format
ValueCountFrequency (%)
11
100.0%
Control
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192736
83.6%
Common 36524
 
15.8%
Han 374
 
0.2%
Arabic 318
 
0.1%
Cyrillic 252
 
0.1%
Hiragana 203
 
0.1%
Katakana 196
 
0.1%
Thai 68
 
< 0.1%
Hangul 9
 
< 0.1%
Inherited 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
15
 
4.0%
8
 
2.1%
7
 
1.9%
7
 
1.9%
7
 
1.9%
6
 
1.6%
6
 
1.6%
5
 
1.3%
5
 
1.3%
5
 
1.3%
Other values (181) 303
81.0%
Latin
ValueCountFrequency (%)
e 18578
 
9.6%
n 15249
 
7.9%
a 14385
 
7.5%
o 14190
 
7.4%
i 14118
 
7.3%
t 13094
 
6.8%
r 12771
 
6.6%
s 10709
 
5.6%
l 6687
 
3.5%
c 6233
 
3.2%
Other values (70) 66722
34.6%
Common
ValueCountFrequency (%)
29897
81.9%
: 1348
 
3.7%
- 1021
 
2.8%
1 496
 
1.4%
, 449
 
1.2%
2 337
 
0.9%
0 322
 
0.9%
& 312
 
0.9%
! 273
 
0.7%
. 242
 
0.7%
Other values (56) 1827
 
5.0%
Katakana
ValueCountFrequency (%)
19
 
9.7%
17
 
8.7%
14
 
7.1%
13
 
6.6%
10
 
5.1%
10
 
5.1%
8
 
4.1%
7
 
3.6%
7
 
3.6%
6
 
3.1%
Other values (37) 85
43.4%
Hiragana
ValueCountFrequency (%)
22
 
10.8%
19
 
9.4%
18
 
8.9%
12
 
5.9%
12
 
5.9%
8
 
3.9%
7
 
3.4%
7
 
3.4%
7
 
3.4%
6
 
3.0%
Other values (32) 85
41.9%
Cyrillic
ValueCountFrequency (%)
а 33
13.1%
о 23
 
9.1%
н 22
 
8.7%
и 20
 
7.9%
р 16
 
6.3%
в 14
 
5.6%
т 14
 
5.6%
е 14
 
5.6%
л 10
 
4.0%
с 9
 
3.6%
Other values (24) 77
30.6%
Thai
ValueCountFrequency (%)
7
 
10.3%
6
 
8.8%
5
 
7.4%
5
 
7.4%
4
 
5.9%
4
 
5.9%
3
 
4.4%
3
 
4.4%
2
 
2.9%
2
 
2.9%
Other values (19) 27
39.7%
Arabic
ValueCountFrequency (%)
ل 52
16.4%
ا 46
14.5%
و 24
 
7.5%
ي 22
 
6.9%
م 20
 
6.3%
ر 19
 
6.0%
س 15
 
4.7%
ة 14
 
4.4%
ت 14
 
4.4%
ب 14
 
4.4%
Other values (18) 78
24.5%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Inherited
ValueCountFrequency (%)
ّ 2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228562
99.1%
None 599
 
0.3%
CJK 374
 
0.2%
Arabic 320
 
0.1%
Cyrillic 252
 
0.1%
Katakana 219
 
0.1%
Hiragana 203
 
0.1%
Thai 68
 
< 0.1%
Punctuation 67
 
< 0.1%
Hangul 9
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29897
 
13.1%
e 18578
 
8.1%
n 15249
 
6.7%
a 14385
 
6.3%
o 14190
 
6.2%
i 14118
 
6.2%
t 13094
 
5.7%
r 12771
 
5.6%
s 10709
 
4.7%
l 6687
 
2.9%
Other values (79) 78884
34.5%
None
ValueCountFrequency (%)
ó 177
29.5%
á 76
12.7%
í 62
 
10.4%
é 54
 
9.0%
ñ 31
 
5.2%
ã 25
 
4.2%
ç 24
 
4.0%
® 17
 
2.8%
ú 15
 
2.5%
  12
 
2.0%
Other values (35) 106
17.7%
Arabic
ValueCountFrequency (%)
ل 52
16.2%
ا 46
14.4%
و 24
 
7.5%
ي 22
 
6.9%
م 20
 
6.2%
ر 19
 
5.9%
س 15
 
4.7%
ة 14
 
4.4%
ت 14
 
4.4%
ب 14
 
4.4%
Other values (19) 80
25.0%
Cyrillic
ValueCountFrequency (%)
а 33
13.1%
о 23
 
9.1%
н 22
 
8.7%
и 20
 
7.9%
р 16
 
6.3%
в 14
 
5.6%
т 14
 
5.6%
е 14
 
5.6%
л 10
 
4.0%
с 9
 
3.6%
Other values (24) 77
30.6%
Punctuation
ValueCountFrequency (%)
30
44.8%
25
37.3%
11
 
16.4%
1
 
1.5%
Katakana
ValueCountFrequency (%)
23
 
10.5%
19
 
8.7%
17
 
7.8%
14
 
6.4%
13
 
5.9%
10
 
4.6%
10
 
4.6%
8
 
3.7%
7
 
3.2%
7
 
3.2%
Other values (38) 91
41.6%
Hiragana
ValueCountFrequency (%)
22
 
10.8%
19
 
9.4%
18
 
8.9%
12
 
5.9%
12
 
5.9%
8
 
3.9%
7
 
3.4%
7
 
3.4%
7
 
3.4%
6
 
3.0%
Other values (32) 85
41.9%
CJK
ValueCountFrequency (%)
15
 
4.0%
8
 
2.1%
7
 
1.9%
7
 
1.9%
7
 
1.9%
6
 
1.6%
6
 
1.6%
5
 
1.3%
5
 
1.3%
5
 
1.3%
Other values (181) 303
81.0%
Thai
ValueCountFrequency (%)
7
 
10.3%
6
 
8.8%
5
 
7.4%
5
 
7.4%
4
 
5.9%
4
 
5.9%
3
 
4.4%
3
 
4.4%
2
 
2.9%
2
 
2.9%
Other values (19) 27
39.7%
Number Forms
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
VS
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Distinct230
Distinct (%)14.4%
Missing3672
Missing (%)69.7%
Memory size41.3 KiB
2023-06-15T08:21:23.725903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length104
Median length47
Mean length24.823419
Min length3

Characters and Unicode

Total characters39643
Distinct characters72
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)3.8%

Sample

1st rowStanford University
2nd rowUniversity of Alberta
3rd rowYale University
4th rowGoogle
5th rowGoogle - Spectrum Sharing
ValueCountFrequency (%)
university 922
 
18.2%
of 634
 
12.5%
the 185
 
3.6%
de 152
 
3.0%
technology 127
 
2.5%
harvard 103
 
2.0%
institute 96
 
1.9%
universidad 90
 
1.8%
california 74
 
1.5%
college 71
 
1.4%
Other values (326) 2623
51.7%
2023-06-15T08:21:24.847463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 3804
 
9.6%
e 3547
 
8.9%
3488
 
8.8%
n 3147
 
7.9%
a 2281
 
5.8%
r 2272
 
5.7%
o 2269
 
5.7%
t 2226
 
5.6%
s 2024
 
5.1%
v 1429
 
3.6%
Other values (62) 13156
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31183
78.7%
Uppercase Letter 4706
 
11.9%
Space Separator 3488
 
8.8%
Other Punctuation 121
 
0.3%
Dash Punctuation 106
 
0.3%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Connector Punctuation 6
 
< 0.1%
Decimal Number 5
 
< 0.1%
Final Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 3804
12.2%
e 3547
11.4%
n 3147
10.1%
a 2281
 
7.3%
r 2272
 
7.3%
o 2269
 
7.3%
t 2226
 
7.1%
s 2024
 
6.5%
v 1429
 
4.6%
y 1358
 
4.4%
Other values (24) 6826
21.9%
Uppercase Letter
ValueCountFrequency (%)
U 1183
25.1%
T 402
 
8.5%
I 365
 
7.8%
C 361
 
7.7%
M 353
 
7.5%
S 239
 
5.1%
A 205
 
4.4%
B 202
 
4.3%
D 192
 
4.1%
H 176
 
3.7%
Other values (17) 1028
21.8%
Other Punctuation
ValueCountFrequency (%)
, 81
66.9%
& 23
 
19.0%
. 17
 
14.0%
Space Separator
ValueCountFrequency (%)
3488
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Decimal Number
ValueCountFrequency (%)
3 5
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35889
90.5%
Common 3754
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 3804
 
10.6%
e 3547
 
9.9%
n 3147
 
8.8%
a 2281
 
6.4%
r 2272
 
6.3%
o 2269
 
6.3%
t 2226
 
6.2%
s 2024
 
5.6%
v 1429
 
4.0%
y 1358
 
3.8%
Other values (51) 11532
32.1%
Common
ValueCountFrequency (%)
3488
92.9%
- 106
 
2.8%
, 81
 
2.2%
& 23
 
0.6%
. 17
 
0.5%
( 12
 
0.3%
) 12
 
0.3%
_ 6
 
0.2%
3 5
 
0.1%
3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39438
99.5%
None 202
 
0.5%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 3804
 
9.6%
e 3547
 
9.0%
3488
 
8.8%
n 3147
 
8.0%
a 2281
 
5.8%
r 2272
 
5.8%
o 2269
 
5.8%
t 2226
 
5.6%
s 2024
 
5.1%
v 1429
 
3.6%
Other values (50) 12951
32.8%
None
ValueCountFrequency (%)
ó 62
30.7%
è 47
23.3%
é 39
19.3%
á 16
 
7.9%
à 11
 
5.4%
É 9
 
4.5%
ä 5
 
2.5%
ü 5
 
2.5%
ò 4
 
2.0%
ã 3
 
1.5%
Punctuation
ValueCountFrequency (%)
3
100.0%

url
Text

Distinct5269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:25.391481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length113
Median length77
Mean length59.307838
Min length29

Characters and Unicode

Total characters312493
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5269 ?
Unique (%)100.0%

Sample

1st rowhttps://www.coursera.org/learn/machine-learning
2nd rowhttps://www.coursera.org/learn/indigenous-canada
3rd rowhttps://www.coursera.org/learn/the-science-of-well-being
4th rowhttps://www.coursera.org/learn/technical-support-fundamentals
5th rowhttps://www.coursera.org/learn/google-cbrs-cpi-training
ValueCountFrequency (%)
https://www.coursera.org/learn/machine-learning 1
 
< 0.1%
https://www.coursera.org/learn/food-and-health 1
 
< 0.1%
https://www.coursera.org/learn/technical-support-fundamentals 1
 
< 0.1%
https://www.coursera.org/learn/google-cbrs-cpi-training 1
 
< 0.1%
https://www.coursera.org/learn/financial-markets-global 1
 
< 0.1%
https://www.coursera.org/learn/introduction-psychology 1
 
< 0.1%
https://www.coursera.org/learn/python 1
 
< 0.1%
https://www.coursera.org/learn/computer-networking 1
 
< 0.1%
https://www.coursera.org/learn/ai-for-everyone 1
 
< 0.1%
https://www.coursera.org/learn/python-crash-course 1
 
< 0.1%
Other values (5259) 5259
99.8%
2023-06-15T08:21:26.360612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22929
 
7.3%
t 22919
 
7.3%
- 21952
 
7.0%
/ 21076
 
6.7%
o 19222
 
6.2%
s 18055
 
5.8%
w 17671
 
5.7%
r 15701
 
5.0%
a 15104
 
4.8%
n 13470
 
4.3%
Other values (40) 124394
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251994
80.6%
Other Punctuation 36883
 
11.8%
Dash Punctuation 21952
 
7.0%
Decimal Number 1614
 
0.5%
Connector Punctuation 34
 
< 0.1%
Uppercase Letter 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22929
 
9.1%
t 22919
 
9.1%
o 19222
 
7.6%
s 18055
 
7.2%
w 17671
 
7.0%
r 15701
 
6.2%
a 15104
 
6.0%
n 13470
 
5.3%
i 12901
 
5.1%
c 12696
 
5.0%
Other values (16) 81326
32.3%
Decimal Number
ValueCountFrequency (%)
1 419
26.0%
2 355
22.0%
0 250
15.5%
3 173
10.7%
5 167
 
10.3%
4 86
 
5.3%
6 57
 
3.5%
7 48
 
3.0%
8 34
 
2.1%
9 25
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
I 3
18.8%
M 3
18.8%
A 3
18.8%
C 2
12.5%
P 1
 
6.2%
D 1
 
6.2%
E 1
 
6.2%
S 1
 
6.2%
O 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 21076
57.1%
. 10538
28.6%
: 5269
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 21952
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252010
80.6%
Common 60483
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22929
 
9.1%
t 22919
 
9.1%
o 19222
 
7.6%
s 18055
 
7.2%
w 17671
 
7.0%
r 15701
 
6.2%
a 15104
 
6.0%
n 13470
 
5.3%
i 12901
 
5.1%
c 12696
 
5.0%
Other values (25) 81342
32.3%
Common
ValueCountFrequency (%)
- 21952
36.3%
/ 21076
34.8%
. 10538
17.4%
: 5269
 
8.7%
1 419
 
0.7%
2 355
 
0.6%
0 250
 
0.4%
3 173
 
0.3%
5 167
 
0.3%
4 86
 
0.1%
Other values (5) 198
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22929
 
7.3%
t 22919
 
7.3%
- 21952
 
7.0%
/ 21076
 
6.7%
o 19222
 
6.2%
s 18055
 
5.8%
w 17671
 
5.7%
r 15701
 
5.0%
a 15104
 
4.8%
n 13470
 
4.3%
Other values (40) 124394
39.8%

id
Text

Distinct4295
Distinct (%)100.0%
Missing974
Missing (%)18.5%
Memory size41.3 KiB
2023-06-15T08:21:26.870481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length82
Median length6
Mean length8.563213
Min length2

Characters and Unicode

Total characters36779
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4295 ?
Unique (%)100.0%

Sample

1st rowmachine-learning
2nd rowindigenous-canada
3rd rowthe-science-of-well-being
4th rowtechnical-support-fundamentals
5th rowgoogle-cbrs-cpi-training
ValueCountFrequency (%)
machine-learning 1
 
< 0.1%
uva-darden-project-management 1
 
< 0.1%
os-power-user 1
 
< 0.1%
the-science-of-well-being 1
 
< 0.1%
technical-support-fundamentals 1
 
< 0.1%
google-cbrs-cpi-training 1
 
< 0.1%
financial-markets-global 1
 
< 0.1%
introduction-psychology 1
 
< 0.1%
python 1
 
< 0.1%
computer-networking 1
 
< 0.1%
Other values (4285) 4285
99.8%
2023-06-15T08:21:27.858325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2867
 
7.8%
6 2634
 
7.2%
2 2593
 
7.1%
4 2526
 
6.9%
8 2418
 
6.6%
0 2396
 
6.5%
5 1833
 
5.0%
9 1821
 
5.0%
7 1815
 
4.9%
3 1736
 
4.7%
Other values (27) 14140
38.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22639
61.6%
Lowercase Letter 12982
35.3%
Dash Punctuation 1158
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1247
 
9.6%
n 1214
 
9.4%
i 1208
 
9.3%
a 1197
 
9.2%
t 1019
 
7.8%
o 925
 
7.1%
s 842
 
6.5%
r 794
 
6.1%
c 710
 
5.5%
l 598
 
4.6%
Other values (16) 3228
24.9%
Decimal Number
ValueCountFrequency (%)
1 2867
12.7%
6 2634
11.6%
2 2593
11.5%
4 2526
11.2%
8 2418
10.7%
0 2396
10.6%
5 1833
8.1%
9 1821
8.0%
7 1815
8.0%
3 1736
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 1158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23797
64.7%
Latin 12982
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1247
 
9.6%
n 1214
 
9.4%
i 1208
 
9.3%
a 1197
 
9.2%
t 1019
 
7.8%
o 925
 
7.1%
s 842
 
6.5%
r 794
 
6.1%
c 710
 
5.5%
l 598
 
4.6%
Other values (16) 3228
24.9%
Common
ValueCountFrequency (%)
1 2867
12.0%
6 2634
11.1%
2 2593
10.9%
4 2526
10.6%
8 2418
10.2%
0 2396
10.1%
5 1833
7.7%
9 1821
7.7%
7 1815
7.6%
3 1736
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2867
 
7.8%
6 2634
 
7.2%
2 2593
 
7.1%
4 2526
 
6.9%
8 2418
 
6.6%
0 2396
 
6.5%
5 1833
 
5.0%
9 1821
 
5.0%
7 1815
 
4.9%
3 1736
 
4.7%
Other values (27) 14140
38.4%

mooc
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.3 KiB
udemy
3672 
edx
974 
coursera
623 

Length

Max length8
Median length5
Mean length4.9850066
Min length3

Characters and Unicode

Total characters26266
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcoursera
2nd rowcoursera
3rd rowcoursera
4th rowcoursera
5th rowcoursera

Common Values

ValueCountFrequency (%)
udemy 3672
69.7%
edx 974
 
18.5%
coursera 623
 
11.8%

Length

2023-06-15T08:21:28.245342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T08:21:28.616353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
udemy 3672
69.7%
edx 974
 
18.5%
coursera 623
 
11.8%

Most occurring characters

ValueCountFrequency (%)
e 5269
20.1%
d 4646
17.7%
u 4295
16.4%
m 3672
14.0%
y 3672
14.0%
r 1246
 
4.7%
x 974
 
3.7%
c 623
 
2.4%
o 623
 
2.4%
s 623
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26266
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5269
20.1%
d 4646
17.7%
u 4295
16.4%
m 3672
14.0%
y 3672
14.0%
r 1246
 
4.7%
x 974
 
3.7%
c 623
 
2.4%
o 623
 
2.4%
s 623
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 26266
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5269
20.1%
d 4646
17.7%
u 4295
16.4%
m 3672
14.0%
y 3672
14.0%
r 1246
 
4.7%
x 974
 
3.7%
c 623
 
2.4%
o 623
 
2.4%
s 623
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5269
20.1%
d 4646
17.7%
u 4295
16.4%
m 3672
14.0%
y 3672
14.0%
r 1246
 
4.7%
x 974
 
3.7%
c 623
 
2.4%
o 623
 
2.4%
s 623
 
2.4%
Distinct887
Distinct (%)96.3%
Missing4348
Missing (%)82.5%
Memory size41.3 KiB
2023-06-15T08:21:29.112360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length783
Median length245
Mean length150.54723
Min length39

Characters and Unicode

Total characters138654
Distinct characters385
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique883 ?
Unique (%)95.9%

Sample

1st rowLearn essential strategies for successful online learning
2nd rowThis course is a "no prerequisite" introduction to Python Programming. You will learn about variables, conditional execution, repeated execution and how we use functions. The homework is done in a web browser so you can do all of the programming assignments on a phone or public computer.
3rd rowAn introduction to the intellectual enterprises of computer science and the art of programming.
4th rowThrough inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
5th rowThis course is part of a MicroMasters® Program
ValueCountFrequency (%)
and 1055
 
5.1%
the 764
 
3.7%
to 613
 
2.9%
of 545
 
2.6%
learn 395
 
1.9%
a 352
 
1.7%
de 348
 
1.7%
in 303
 
1.5%
how 276
 
1.3%
y 224
 
1.1%
Other values (4749) 15978
76.6%
2023-06-15T08:21:30.148467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19939
14.4%
e 13093
 
9.4%
a 10335
 
7.5%
n 9176
 
6.6%
o 8890
 
6.4%
i 8709
 
6.3%
t 8382
 
6.0%
s 7847
 
5.7%
r 7649
 
5.5%
l 5049
 
3.6%
Other values (375) 39585
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112333
81.0%
Space Separator 19939
 
14.4%
Uppercase Letter 2823
 
2.0%
Other Punctuation 2321
 
1.7%
Other Letter 703
 
0.5%
Dash Punctuation 215
 
0.2%
Decimal Number 134
 
0.1%
Final Punctuation 58
 
< 0.1%
Open Punctuation 43
 
< 0.1%
Close Punctuation 42
 
< 0.1%
Other values (5) 43
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ل 34
 
4.8%
ا 34
 
4.8%
26
 
3.7%
م 20
 
2.8%
و 17
 
2.4%
ت 17
 
2.4%
ي 16
 
2.3%
15
 
2.1%
ة 12
 
1.7%
ع 10
 
1.4%
Other values (261) 502
71.4%
Lowercase Letter
ValueCountFrequency (%)
e 13093
11.7%
a 10335
 
9.2%
n 9176
 
8.2%
o 8890
 
7.9%
i 8709
 
7.8%
t 8382
 
7.5%
s 7847
 
7.0%
r 7649
 
6.8%
l 5049
 
4.5%
c 5001
 
4.5%
Other values (32) 28202
25.1%
Uppercase Letter
ValueCountFrequency (%)
L 440
15.6%
A 270
9.6%
T 270
9.6%
E 205
 
7.3%
C 197
 
7.0%
I 177
 
6.3%
P 171
 
6.1%
M 170
 
6.0%
S 163
 
5.8%
D 152
 
5.4%
Other values (16) 608
21.5%
Other Punctuation
ValueCountFrequency (%)
. 1067
46.0%
, 1023
44.1%
' 45
 
1.9%
? 40
 
1.7%
! 30
 
1.3%
" 26
 
1.1%
: 24
 
1.0%
15
 
0.6%
11
 
0.5%
9
 
0.4%
Other values (10) 31
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 37
27.6%
1 31
23.1%
2 18
13.4%
5 15
11.2%
4 10
 
7.5%
3 9
 
6.7%
8 5
 
3.7%
9 5
 
3.7%
7 3
 
2.2%
6 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 204
94.9%
7
 
3.3%
4
 
1.9%
Final Punctuation
ValueCountFrequency (%)
50
86.2%
7
 
12.1%
» 1
 
1.7%
Initial Punctuation
ValueCountFrequency (%)
7
87.5%
« 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
+ 4
80.0%
| 1
 
20.0%
Space Separator
ValueCountFrequency (%)
19939
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Other Symbol
ValueCountFrequency (%)
® 25
100.0%
Format
ValueCountFrequency (%)
3
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115156
83.1%
Common 22795
 
16.4%
Han 466
 
0.3%
Arabic 237
 
0.2%

Most frequent character per script

Han
ValueCountFrequency (%)
26
 
5.6%
15
 
3.2%
10
 
2.1%
8
 
1.7%
7
 
1.5%
7
 
1.5%
7
 
1.5%
6
 
1.3%
6
 
1.3%
5
 
1.1%
Other values (231) 369
79.2%
Latin
ValueCountFrequency (%)
e 13093
11.4%
a 10335
 
9.0%
n 9176
 
8.0%
o 8890
 
7.7%
i 8709
 
7.6%
t 8382
 
7.3%
s 7847
 
6.8%
r 7649
 
6.6%
l 5049
 
4.4%
c 5001
 
4.3%
Other values (58) 31025
26.9%
Common
ValueCountFrequency (%)
19939
87.5%
. 1067
 
4.7%
, 1023
 
4.5%
- 204
 
0.9%
50
 
0.2%
' 45
 
0.2%
( 43
 
0.2%
) 42
 
0.2%
? 40
 
0.2%
0 37
 
0.2%
Other values (36) 305
 
1.3%
Arabic
ValueCountFrequency (%)
ل 34
14.3%
ا 34
14.3%
م 20
 
8.4%
و 17
 
7.2%
ت 17
 
7.2%
ي 16
 
6.8%
ة 12
 
5.1%
ع 10
 
4.2%
ف 8
 
3.4%
ب 6
 
2.5%
Other values (20) 63
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137243
99.0%
None 626
 
0.5%
CJK 466
 
0.3%
Arabic 241
 
0.2%
Punctuation 78
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19939
14.5%
e 13093
 
9.5%
a 10335
 
7.5%
n 9176
 
6.7%
o 8890
 
6.5%
i 8709
 
6.3%
t 8382
 
6.1%
s 7847
 
5.7%
r 7649
 
5.6%
l 5049
 
3.7%
Other values (71) 38174
27.8%
None
ValueCountFrequency (%)
ó 184
29.4%
á 146
23.3%
í 84
13.4%
é 80
12.8%
® 25
 
4.0%
ñ 20
 
3.2%
ú 18
 
2.9%
15
 
2.4%
11
 
1.8%
9
 
1.4%
Other values (16) 34
 
5.4%
Punctuation
ValueCountFrequency (%)
50
64.1%
7
 
9.0%
7
 
9.0%
7
 
9.0%
4
 
5.1%
3
 
3.8%
Arabic
ValueCountFrequency (%)
ل 34
14.1%
ا 34
14.1%
م 20
 
8.3%
و 17
 
7.1%
ت 17
 
7.1%
ي 16
 
6.6%
ة 12
 
5.0%
ع 10
 
4.1%
ف 8
 
3.3%
ب 6
 
2.5%
Other values (21) 67
27.8%
CJK
ValueCountFrequency (%)
26
 
5.6%
15
 
3.2%
10
 
2.1%
8
 
1.7%
7
 
1.5%
7
 
1.5%
7
 
1.5%
6
 
1.3%
6
 
1.3%
5
 
1.1%
Other values (231) 369
79.2%

n_subscribers
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct3033
Distinct (%)67.0%
Missing743
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean12628.401
Minimum0
Maximum2442271
Zeros65
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:30.564445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1214
median1423.5
Q37303.75
95-th percentile55996.25
Maximum2442271
Range2442271
Interquartile range (IQR)7089.75

Descriptive statistics

Standard deviation55943.377
Coefficient of variation (CV)4.4299651
Kurtosis857.41469
Mean12628.401
Median Absolute Deviation (MAD)1389.5
Skewness23.310188
Sum57156144
Variance3.1296614 × 109
MonotonicityNot monotonic
2023-06-15T08:21:30.982316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
1.2%
1 49
 
0.9%
5 28
 
0.5%
2 27
 
0.5%
3 26
 
0.5%
4 26
 
0.5%
7 24
 
0.5%
11 23
 
0.4%
13 19
 
0.4%
9 18
 
0.3%
Other values (3023) 4221
80.1%
(Missing) 743
 
14.1%
ValueCountFrequency (%)
0 65
1.2%
1 49
0.9%
2 27
0.5%
3 26
 
0.5%
4 26
 
0.5%
5 28
0.5%
6 18
 
0.3%
7 24
 
0.5%
8 18
 
0.3%
9 18
 
0.3%
ValueCountFrequency (%)
2442271 1
< 0.1%
1103777 1
< 0.1%
1022489 1
< 0.1%
698950 1
< 0.1%
642088 1
< 0.1%
528782 1
< 0.1%
475614 1
< 0.1%
414181 1
< 0.1%
406181 1
< 0.1%
400169 1
< 0.1%

modality
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing4295
Missing (%)81.5%
Memory size41.3 KiB
Self-paced on your time
916 
Instructor-led on a course schedule
 
58

Length

Max length35
Median length23
Mean length23.714579
Min length23

Characters and Unicode

Total characters23098
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-paced on your time
2nd rowSelf-paced on your time
3rd rowSelf-paced on your time
4th rowInstructor-led on a course schedule
5th rowSelf-paced on your time

Common Values

ValueCountFrequency (%)
Self-paced on your time 916
 
17.4%
Instructor-led on a course schedule 58
 
1.1%
(Missing) 4295
81.5%

Length

2023-06-15T08:21:31.334338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T08:21:31.910339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
on 974
24.6%
self-paced 916
23.2%
your 916
23.2%
time 916
23.2%
instructor-led 58
 
1.5%
a 58
 
1.5%
course 58
 
1.5%
schedule 58
 
1.5%

Most occurring characters

ValueCountFrequency (%)
2980
 
12.9%
e 2980
 
12.9%
o 2006
 
8.7%
r 1090
 
4.7%
c 1090
 
4.7%
u 1090
 
4.7%
l 1032
 
4.5%
d 1032
 
4.5%
n 1032
 
4.5%
t 1032
 
4.5%
Other values (11) 7734
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18170
78.7%
Space Separator 2980
 
12.9%
Dash Punctuation 974
 
4.2%
Uppercase Letter 974
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2980
16.4%
o 2006
11.0%
r 1090
 
6.0%
c 1090
 
6.0%
u 1090
 
6.0%
l 1032
 
5.7%
d 1032
 
5.7%
n 1032
 
5.7%
t 1032
 
5.7%
a 974
 
5.4%
Other values (7) 4812
26.5%
Uppercase Letter
ValueCountFrequency (%)
S 916
94.0%
I 58
 
6.0%
Space Separator
ValueCountFrequency (%)
2980
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19144
82.9%
Common 3954
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2980
15.6%
o 2006
 
10.5%
r 1090
 
5.7%
c 1090
 
5.7%
u 1090
 
5.7%
l 1032
 
5.4%
d 1032
 
5.4%
n 1032
 
5.4%
t 1032
 
5.4%
a 974
 
5.1%
Other values (9) 5786
30.2%
Common
ValueCountFrequency (%)
2980
75.4%
- 974
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2980
 
12.9%
e 2980
 
12.9%
o 2006
 
8.7%
r 1090
 
4.7%
c 1090
 
4.7%
u 1090
 
4.7%
l 1032
 
4.5%
d 1032
 
4.5%
n 1032
 
4.5%
t 1032
 
4.5%
Other values (11) 7734
33.5%
Distinct775
Distinct (%)79.8%
Missing4298
Missing (%)81.6%
Memory size41.3 KiB
2023-06-15T08:21:32.422364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length121
Median length85
Mean length33.741504
Min length9

Characters and Unicode

Total characters32763
Distinct characters92
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique675 ?
Unique (%)69.5%

Sample

1st rowNina Huntemann-Robyn Belair-Ben Piscopo
2nd rowCharles Severance
3rd rowDavid J. Malan-Doug Lloyd-Brian Yu
4th rowDimitris Bertsimas-Allison O'Hair-John Silberholz-Iain Dunning
5th rowStephan Sorger
ValueCountFrequency (%)
david 38
 
1.0%
van 29
 
0.8%
de 27
 
0.7%
peter 20
 
0.5%
j 19
 
0.5%
rafael 18
 
0.5%
m 16
 
0.4%
d 14
 
0.4%
thomas 14
 
0.4%
dr 14
 
0.4%
Other values (2371) 3566
94.5%
2023-06-15T08:21:33.501152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3248
 
9.9%
2808
 
8.6%
e 2738
 
8.4%
n 2146
 
6.6%
r 2146
 
6.6%
i 1925
 
5.9%
o 1713
 
5.2%
l 1391
 
4.2%
s 1167
 
3.6%
- 1131
 
3.5%
Other values (82) 12350
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23564
71.9%
Uppercase Letter 4981
 
15.2%
Space Separator 2808
 
8.6%
Dash Punctuation 1132
 
3.5%
Other Punctuation 241
 
0.7%
Other Letter 14
 
< 0.1%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Format 5
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3248
13.8%
e 2738
11.6%
n 2146
 
9.1%
r 2146
 
9.1%
i 1925
 
8.2%
o 1713
 
7.3%
l 1391
 
5.9%
s 1167
 
5.0%
t 986
 
4.2%
h 732
 
3.1%
Other values (30) 5372
22.8%
Uppercase Letter
ValueCountFrequency (%)
M 521
 
10.5%
S 402
 
8.1%
A 387
 
7.8%
C 324
 
6.5%
J 312
 
6.3%
D 311
 
6.2%
P 309
 
6.2%
B 285
 
5.7%
R 282
 
5.7%
G 245
 
4.9%
Other values (20) 1603
32.2%
Other Letter
ValueCountFrequency (%)
ª 2
14.3%
س 2
14.3%
و 2
14.3%
ل 2
14.3%
ج 1
7.1%
ى 1
7.1%
م 1
7.1%
ة 1
7.1%
ا 1
7.1%
ب 1
7.1%
Other Punctuation
ValueCountFrequency (%)
. 210
87.1%
, 24
 
10.0%
' 3
 
1.2%
: 2
 
0.8%
" 2
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 1131
99.9%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
2808
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Format
ValueCountFrequency (%)
5
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28547
87.1%
Common 4204
 
12.8%
Arabic 12
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3248
 
11.4%
e 2738
 
9.6%
n 2146
 
7.5%
r 2146
 
7.5%
i 1925
 
6.7%
o 1713
 
6.0%
l 1391
 
4.9%
s 1167
 
4.1%
t 986
 
3.5%
h 732
 
2.6%
Other values (61) 10355
36.3%
Common
ValueCountFrequency (%)
2808
66.8%
- 1131
26.9%
. 210
 
5.0%
, 24
 
0.6%
( 8
 
0.2%
) 8
 
0.2%
5
 
0.1%
' 3
 
0.1%
: 2
 
< 0.1%
" 2
 
< 0.1%
Other values (2) 3
 
0.1%
Arabic
ValueCountFrequency (%)
س 2
16.7%
و 2
16.7%
ل 2
16.7%
ج 1
8.3%
ى 1
8.3%
م 1
8.3%
ة 1
8.3%
ا 1
8.3%
ب 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32418
98.9%
None 325
 
1.0%
Arabic 12
 
< 0.1%
Punctuation 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3248
 
10.0%
2808
 
8.7%
e 2738
 
8.4%
n 2146
 
6.6%
r 2146
 
6.6%
i 1925
 
5.9%
o 1713
 
5.3%
l 1391
 
4.3%
s 1167
 
3.6%
- 1131
 
3.5%
Other values (51) 12005
37.0%
None
ValueCountFrequency (%)
í 91
28.0%
á 70
21.5%
é 66
20.3%
ó 36
 
11.1%
ñ 18
 
5.5%
Á 16
 
4.9%
ú 10
 
3.1%
ö 3
 
0.9%
ü 2
 
0.6%
ä 2
 
0.6%
Other values (9) 11
 
3.4%
Punctuation
ValueCountFrequency (%)
5
62.5%
2
 
25.0%
1
 
12.5%
Arabic
ValueCountFrequency (%)
س 2
16.7%
و 2
16.7%
ل 2
16.7%
ج 1
8.3%
ى 1
8.3%
م 1
8.3%
ة 1
8.3%
ا 1
8.3%
ب 1
8.3%

level
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.2%
Missing623
Missing (%)11.8%
Memory size41.3 KiB
All Levels
1925 
Beginner Level
1268 
Introductory
621 
Intermediate Level
421 
Intermediate
266 
Other values (2)
 
145

Length

Max length18
Median length14
Mean length12.185966
Min length8

Characters and Unicode

Total characters56616
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntroductory
2nd rowIntroductory
3rd rowIntroductory
4th rowIntermediate
5th rowIntroductory

Common Values

ValueCountFrequency (%)
All Levels 1925
36.5%
Beginner Level 1268
24.1%
Introductory 621
 
11.8%
Intermediate Level 421
 
8.0%
Intermediate 266
 
5.0%
Advanced 87
 
1.7%
Expert Level 58
 
1.1%
(Missing) 623
 
11.8%

Length

2023-06-15T08:21:33.885360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T08:21:34.279547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
all 1925
23.1%
levels 1925
23.1%
level 1747
21.0%
beginner 1268
15.2%
intermediate 687
 
8.3%
introductory 621
 
7.5%
advanced 87
 
1.0%
expert 58
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 12086
21.3%
l 7522
13.3%
n 3931
 
6.9%
v 3759
 
6.6%
3672
 
6.5%
L 3672
 
6.5%
r 3255
 
5.7%
t 2674
 
4.7%
A 2012
 
3.6%
i 1955
 
3.5%
Other values (14) 12078
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44626
78.8%
Uppercase Letter 8318
 
14.7%
Space Separator 3672
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12086
27.1%
l 7522
16.9%
n 3931
 
8.8%
v 3759
 
8.4%
r 3255
 
7.3%
t 2674
 
6.0%
i 1955
 
4.4%
s 1925
 
4.3%
d 1482
 
3.3%
g 1268
 
2.8%
Other values (8) 4769
 
10.7%
Uppercase Letter
ValueCountFrequency (%)
L 3672
44.1%
A 2012
24.2%
I 1308
 
15.7%
B 1268
 
15.2%
E 58
 
0.7%
Space Separator
ValueCountFrequency (%)
3672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52944
93.5%
Common 3672
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12086
22.8%
l 7522
14.2%
n 3931
 
7.4%
v 3759
 
7.1%
L 3672
 
6.9%
r 3255
 
6.1%
t 2674
 
5.1%
A 2012
 
3.8%
i 1955
 
3.7%
s 1925
 
3.6%
Other values (13) 10153
19.2%
Common
ValueCountFrequency (%)
3672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12086
21.3%
l 7522
13.3%
n 3931
 
6.9%
v 3759
 
6.6%
3672
 
6.5%
L 3672
 
6.5%
r 3255
 
5.7%
t 2674
 
4.7%
A 2012
 
3.6%
i 1955
 
3.5%
Other values (14) 12078
21.3%

subject
Categorical

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)0.8%
Missing623
Missing (%)11.8%
Memory size41.3 KiB
Web Development
1199 
Business Finance
1191 
Musical Instruments
680 
Graphic Design
602 
Computer Science
166 
Other values (30)
808 

Length

Max length28
Median length26
Mean length15.876022
Min length3

Characters and Unicode

Total characters73760
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEducation & Teacher Training
2nd rowComputer Science
3rd rowComputer Science
4th rowData Analysis & Statistics
5th rowComputer Science

Common Values

ValueCountFrequency (%)
Web Development 1199
22.8%
Business Finance 1191
22.6%
Musical Instruments 680
12.9%
Graphic Design 602
11.4%
Computer Science 166
 
3.2%
Business & Management 164
 
3.1%
Data Analysis & Statistics 71
 
1.3%
Humanities 64
 
1.2%
Engineering 58
 
1.1%
Social Sciences 51
 
1.0%
Other values (25) 400
 
7.6%
(Missing) 623
11.8%

Length

2023-06-15T08:21:34.671524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
business 1355
14.3%
finance 1237
13.1%
web 1199
12.7%
development 1199
12.7%
musical 680
7.2%
instruments 680
7.2%
design 610
6.4%
graphic 602
6.4%
388
 
4.1%
science 176
 
1.9%
Other values (40) 1344
14.2%

Most occurring characters

ValueCountFrequency (%)
e 10185
13.8%
n 8251
 
11.2%
s 7307
 
9.9%
i 5729
 
7.8%
4824
 
6.5%
a 3532
 
4.8%
t 3492
 
4.7%
c 3457
 
4.7%
u 3109
 
4.2%
m 2418
 
3.3%
Other values (29) 21456
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 59466
80.6%
Uppercase Letter 9082
 
12.3%
Space Separator 4824
 
6.5%
Other Punctuation 388
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10185
17.1%
n 8251
13.9%
s 7307
12.3%
i 5729
9.6%
a 3532
 
5.9%
t 3492
 
5.9%
c 3457
 
5.8%
u 3109
 
5.2%
m 2418
 
4.1%
l 2089
 
3.5%
Other values (11) 9897
16.6%
Uppercase Letter
ValueCountFrequency (%)
D 1880
20.7%
B 1390
15.3%
F 1243
13.7%
W 1199
13.2%
M 913
10.1%
I 680
 
7.5%
G 602
 
6.6%
S 419
 
4.6%
C 222
 
2.4%
E 179
 
2.0%
Other values (6) 355
 
3.9%
Space Separator
ValueCountFrequency (%)
4824
100.0%
Other Punctuation
ValueCountFrequency (%)
& 388
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68548
92.9%
Common 5212
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10185
14.9%
n 8251
12.0%
s 7307
 
10.7%
i 5729
 
8.4%
a 3532
 
5.2%
t 3492
 
5.1%
c 3457
 
5.0%
u 3109
 
4.5%
m 2418
 
3.5%
l 2089
 
3.0%
Other values (27) 18979
27.7%
Common
ValueCountFrequency (%)
4824
92.6%
& 388
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10185
13.8%
n 8251
 
11.2%
s 7307
 
9.9%
i 5729
 
7.8%
4824
 
6.5%
a 3532
 
4.8%
t 3492
 
4.7%
c 3457
 
4.7%
u 3109
 
4.2%
m 2418
 
3.3%
Other values (29) 21456
29.1%

language
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct9
Distinct (%)0.9%
Missing4295
Missing (%)81.5%
Memory size41.3 KiB
English
776 
Español
176 
Français
 
7
Italiano
 
4
中文
 
4
Other values (4)
 
7

Length

Max length13
Median length7
Mean length7.0010267
Min length2

Characters and Unicode

Total characters6819
Distinct characters36
Distinct categories4 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 776
 
14.7%
Español 176
 
3.3%
Français 7
 
0.1%
Italiano 4
 
0.1%
中文 4
 
0.1%
Português 4
 
0.1%
日本語 1
 
< 0.1%
اللغة العربية 1
 
< 0.1%
Deutsch 1
 
< 0.1%
(Missing) 4295
81.5%

Length

2023-06-15T08:21:34.999546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T08:21:35.490632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
english 776
79.6%
español 176
 
18.1%
français 7
 
0.7%
italiano 4
 
0.4%
中文 4
 
0.4%
português 4
 
0.4%
日本語 1
 
0.1%
اللغة 1
 
0.1%
العربية 1
 
0.1%
deutsch 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s 964
14.1%
l 956
14.0%
E 952
14.0%
n 787
11.5%
i 787
11.5%
g 780
11.4%
h 777
11.4%
a 198
 
2.9%
o 184
 
2.7%
p 176
 
2.6%
Other values (26) 258
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5827
85.5%
Uppercase Letter 968
 
14.2%
Other Letter 23
 
0.3%
Space Separator 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 964
16.5%
l 956
16.4%
n 787
13.5%
i 787
13.5%
g 780
13.4%
h 777
13.3%
a 198
 
3.4%
o 184
 
3.2%
p 176
 
3.0%
ñ 176
 
3.0%
Other values (7) 42
 
0.7%
Other Letter
ValueCountFrequency (%)
4
17.4%
4
17.4%
ل 3
13.0%
ا 2
8.7%
ة 2
8.7%
1
 
4.3%
1
 
4.3%
غ 1
 
4.3%
1
 
4.3%
ع 1
 
4.3%
Other values (3) 3
13.0%
Uppercase Letter
ValueCountFrequency (%)
E 952
98.3%
F 7
 
0.7%
P 4
 
0.4%
I 4
 
0.4%
D 1
 
0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6795
99.6%
Arabic 12
 
0.2%
Han 11
 
0.2%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 964
14.2%
l 956
14.1%
E 952
14.0%
n 787
11.6%
i 787
11.6%
g 780
11.5%
h 777
11.4%
a 198
 
2.9%
o 184
 
2.7%
p 176
 
2.6%
Other values (12) 234
 
3.4%
Arabic
ValueCountFrequency (%)
ل 3
25.0%
ا 2
16.7%
ة 2
16.7%
غ 1
 
8.3%
ع 1
 
8.3%
ر 1
 
8.3%
ب 1
 
8.3%
ي 1
 
8.3%
Han
ValueCountFrequency (%)
4
36.4%
4
36.4%
1
 
9.1%
1
 
9.1%
1
 
9.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6609
96.9%
None 187
 
2.7%
Arabic 12
 
0.2%
CJK 11
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 964
14.6%
l 956
14.5%
E 952
14.4%
n 787
11.9%
i 787
11.9%
g 780
11.8%
h 777
11.8%
a 198
 
3.0%
o 184
 
2.8%
p 176
 
2.7%
Other values (10) 48
 
0.7%
None
ValueCountFrequency (%)
ñ 176
94.1%
ç 7
 
3.7%
ê 4
 
2.1%
CJK
ValueCountFrequency (%)
4
36.4%
4
36.4%
1
 
9.1%
1
 
9.1%
1
 
9.1%
Arabic
ValueCountFrequency (%)
ل 3
25.0%
ا 2
16.7%
ة 2
16.7%
غ 1
 
8.3%
ع 1
 
8.3%
ر 1
 
8.3%
ب 1
 
8.3%
ي 1
 
8.3%

subtitles
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct33
Distinct (%)3.4%
Missing4298
Missing (%)81.6%
Memory size41.3 KiB
English
712 
Español
157 
English, 中文
 
21
English, Español
 
21
English, हिन्दी
 
10
Other values (28)
 
50

Length

Max length79
Median length7
Mean length8.015448
Min length7

Characters and Unicode

Total characters7783
Distinct characters61
Distinct categories7 ?
Distinct scripts8 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)2.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English 712
 
13.5%
Español 157
 
3.0%
English, 中文 21
 
0.4%
English, Español 21
 
0.4%
English, हिन्दी 10
 
0.2%
Français 7
 
0.1%
English, Русский 5
 
0.1%
Italiano 4
 
0.1%
Português 4
 
0.1%
English, 日本語 3
 
0.1%
Other values (23) 27
 
0.5%
(Missing) 4298
81.6%

Length

2023-06-15T08:21:35.915118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 796
72.2%
español 190
 
17.2%
中文 37
 
3.4%
français 16
 
1.5%
हिन्दी 11
 
1.0%
português 11
 
1.0%
русский 9
 
0.8%
italiano 6
 
0.5%
日本語 6
 
0.5%
اللغة 6
 
0.5%
Other values (6) 15
 
1.4%

Most occurring characters

ValueCountFrequency (%)
s 1017
13.1%
l 992
12.7%
E 986
12.7%
n 821
10.5%
i 819
10.5%
g 807
10.4%
h 799
10.3%
a 235
 
3.0%
o 208
 
2.7%
ñ 190
 
2.4%
Other values (51) 909
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6244
80.2%
Uppercase Letter 1033
 
13.3%
Other Letter 215
 
2.8%
Space Separator 132
 
1.7%
Other Punctuation 126
 
1.6%
Spacing Mark 22
 
0.3%
Nonspacing Mark 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1017
16.3%
l 992
15.9%
n 821
13.1%
i 819
13.1%
g 807
12.9%
h 799
12.8%
a 235
 
3.8%
o 208
 
3.3%
ñ 190
 
3.0%
p 190
 
3.0%
Other values (15) 166
 
2.7%
Other Letter
ValueCountFrequency (%)
37
17.2%
37
17.2%
ل 18
 
8.4%
ة 12
 
5.6%
ا 12
 
5.6%
11
 
5.1%
11
 
5.1%
11
 
5.1%
6
 
2.8%
غ 6
 
2.8%
Other values (14) 54
25.1%
Uppercase Letter
ValueCountFrequency (%)
E 986
95.5%
F 16
 
1.5%
P 11
 
1.1%
Р 9
 
0.9%
I 7
 
0.7%
D 3
 
0.3%
T 1
 
0.1%
Spacing Mark
ValueCountFrequency (%)
11
50.0%
ि 11
50.0%
Space Separator
ValueCountFrequency (%)
132
100.0%
Other Punctuation
ValueCountFrequency (%)
, 126
100.0%
Nonspacing Mark
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7214
92.7%
Common 258
 
3.3%
Han 92
 
1.2%
Arabic 72
 
0.9%
Devanagari 66
 
0.8%
Cyrillic 63
 
0.8%
Hebrew 15
 
0.2%
Hangul 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1017
14.1%
l 992
13.8%
E 986
13.7%
n 821
11.4%
i 819
11.4%
g 807
11.2%
h 799
11.1%
a 235
 
3.3%
o 208
 
2.9%
ñ 190
 
2.6%
Other values (16) 340
 
4.7%
Arabic
ValueCountFrequency (%)
ل 18
25.0%
ة 12
16.7%
ا 12
16.7%
غ 6
 
8.3%
ع 6
 
8.3%
ر 6
 
8.3%
ب 6
 
8.3%
ي 6
 
8.3%
Cyrillic
ValueCountFrequency (%)
с 18
28.6%
й 9
14.3%
к 9
14.3%
у 9
14.3%
Р 9
14.3%
и 9
14.3%
Devanagari
ValueCountFrequency (%)
11
16.7%
11
16.7%
11
16.7%
ि 11
16.7%
11
16.7%
11
16.7%
Han
ValueCountFrequency (%)
37
40.2%
37
40.2%
6
 
6.5%
6
 
6.5%
6
 
6.5%
Hebrew
ValueCountFrequency (%)
ת 3
20.0%
י 3
20.0%
ר 3
20.0%
ב 3
20.0%
ע 3
20.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Common
ValueCountFrequency (%)
132
51.2%
, 126
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7253
93.2%
None 219
 
2.8%
CJK 92
 
1.2%
Arabic 72
 
0.9%
Devanagari 66
 
0.8%
Cyrillic 63
 
0.8%
Hebrew 15
 
0.2%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1017
14.0%
l 992
13.7%
E 986
13.6%
n 821
11.3%
i 819
11.3%
g 807
11.1%
h 799
11.0%
a 235
 
3.2%
o 208
 
2.9%
p 190
 
2.6%
Other values (14) 379
 
5.2%
None
ValueCountFrequency (%)
ñ 190
86.8%
ç 17
 
7.8%
ê 11
 
5.0%
ü 1
 
0.5%
CJK
ValueCountFrequency (%)
37
40.2%
37
40.2%
6
 
6.5%
6
 
6.5%
6
 
6.5%
Cyrillic
ValueCountFrequency (%)
с 18
28.6%
й 9
14.3%
к 9
14.3%
у 9
14.3%
Р 9
14.3%
и 9
14.3%
Arabic
ValueCountFrequency (%)
ل 18
25.0%
ة 12
16.7%
ا 12
16.7%
غ 6
 
8.3%
ع 6
 
8.3%
ر 6
 
8.3%
ب 6
 
8.3%
ي 6
 
8.3%
Devanagari
ValueCountFrequency (%)
11
16.7%
11
16.7%
11
16.7%
ि 11
16.7%
11
16.7%
11
16.7%
Hebrew
ValueCountFrequency (%)
ת 3
20.0%
י 3
20.0%
ר 3
20.0%
ב 3
20.0%
ע 3
20.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

effort
Text

Distinct53
Distinct (%)5.4%
Missing4295
Missing (%)81.5%
Memory size41.3 KiB
2023-06-15T08:21:36.351297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length18
Mean length18.205339
Min length18

Characters and Unicode

Total characters17732
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.7%

Sample

1st row2–3 hours per week
2nd row2–4 hours per week
3rd row6–18 hours per week
4th row10–15 hours per week
5th row5–7 hours per week
ValueCountFrequency (%)
hours 974
25.0%
per 974
25.0%
week 974
25.0%
2–4 108
 
2.8%
2–3 104
 
2.7%
3–5 103
 
2.6%
3–4 91
 
2.3%
4–6 79
 
2.0%
8–10 57
 
1.5%
1–2 55
 
1.4%
Other values (46) 377
 
9.7%
2023-06-15T08:21:37.144241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2922
16.5%
2922
16.5%
r 1948
11.0%
h 974
 
5.5%
o 974
 
5.5%
u 974
 
5.5%
s 974
 
5.5%
p 974
 
5.5%
974
 
5.5%
w 974
 
5.5%
Other values (11) 3122
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11688
65.9%
Space Separator 2922
 
16.5%
Decimal Number 2148
 
12.1%
Dash Punctuation 974
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 357
16.6%
3 351
16.3%
2 331
15.4%
1 282
13.1%
5 275
12.8%
6 185
8.6%
8 156
7.3%
0 150
7.0%
7 44
 
2.0%
9 17
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 2922
25.0%
r 1948
16.7%
h 974
 
8.3%
o 974
 
8.3%
u 974
 
8.3%
s 974
 
8.3%
p 974
 
8.3%
w 974
 
8.3%
k 974
 
8.3%
Space Separator
ValueCountFrequency (%)
2922
100.0%
Dash Punctuation
ValueCountFrequency (%)
974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11688
65.9%
Common 6044
34.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2922
48.3%
974
 
16.1%
4 357
 
5.9%
3 351
 
5.8%
2 331
 
5.5%
1 282
 
4.7%
5 275
 
4.5%
6 185
 
3.1%
8 156
 
2.6%
0 150
 
2.5%
Other values (2) 61
 
1.0%
Latin
ValueCountFrequency (%)
e 2922
25.0%
r 1948
16.7%
h 974
 
8.3%
o 974
 
8.3%
u 974
 
8.3%
s 974
 
8.3%
p 974
 
8.3%
w 974
 
8.3%
k 974
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16758
94.5%
Punctuation 974
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2922
17.4%
2922
17.4%
r 1948
11.6%
h 974
 
5.8%
o 974
 
5.8%
u 974
 
5.8%
s 974
 
5.8%
p 974
 
5.8%
w 974
 
5.8%
k 974
 
5.8%
Other values (10) 2148
12.8%
Punctuation
ValueCountFrequency (%)
974
100.0%
Distinct123
Distinct (%)2.6%
Missing623
Missing (%)11.8%
Memory size41.3 KiB
2023-06-15T08:21:37.640218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length3
Mean length4.7178218
Min length3

Characters and Unicode

Total characters21919
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.7%

Sample

1st row2 Weeks
2nd row7 Weeks
3rd row12 Weeks
4th row13 Weeks
5th row4 Weeks
ValueCountFrequency (%)
weeks 974
17.3%
1.0 606
 
10.8%
1.5 506
 
9.0%
2.0 419
 
7.5%
2.5 269
 
4.8%
3.0 248
 
4.4%
4 194
 
3.5%
6 187
 
3.3%
3.5 182
 
3.2%
5 148
 
2.6%
Other values (114) 1887
33.6%
2023-06-15T08:21:38.501292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3672
16.8%
0 2335
10.7%
6 2191
10.0%
3 2178
9.9%
5 2030
9.3%
e 1948
8.9%
1 1654
7.5%
974
 
4.4%
W 974
 
4.4%
k 974
 
4.4%
Other values (6) 2989
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12403
56.6%
Lowercase Letter 3896
 
17.8%
Other Punctuation 3672
 
16.8%
Space Separator 974
 
4.4%
Uppercase Letter 974
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2335
18.8%
6 2191
17.7%
3 2178
17.6%
5 2030
16.4%
1 1654
13.3%
2 841
 
6.8%
4 532
 
4.3%
7 349
 
2.8%
8 214
 
1.7%
9 79
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
e 1948
50.0%
k 974
25.0%
s 974
25.0%
Other Punctuation
ValueCountFrequency (%)
. 3672
100.0%
Space Separator
ValueCountFrequency (%)
974
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17049
77.8%
Latin 4870
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3672
21.5%
0 2335
13.7%
6 2191
12.9%
3 2178
12.8%
5 2030
11.9%
1 1654
9.7%
974
 
5.7%
2 841
 
4.9%
4 532
 
3.1%
7 349
 
2.0%
Other values (2) 293
 
1.7%
Latin
ValueCountFrequency (%)
e 1948
40.0%
W 974
20.0%
k 974
20.0%
s 974
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3672
16.8%
0 2335
10.7%
6 2191
10.0%
3 2178
9.9%
5 2030
9.3%
e 1948
8.9%
1 1654
7.5%
974
 
4.4%
W 974
 
4.4%
k 974
 
4.4%
Other values (6) 2989
13.6%

price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)4.8%
Missing4295
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean227.40452
Minimum5
Maximum39960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:38.941304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q149
median79
Q3149
95-th percentile249
Maximum39960
Range39955
Interquartile range (IQR)100

Descriptive statistics

Standard deviation1891.6947
Coefficient of variation (CV)8.3186328
Kurtosis324.84395
Mean227.40452
Median Absolute Deviation (MAD)30
Skewness17.630244
Sum221492
Variance3578508.8
MonotonicityNot monotonic
2023-06-15T08:21:39.325305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
49 270
 
5.1%
99 136
 
2.6%
50 96
 
1.8%
199 85
 
1.6%
149 78
 
1.5%
25 49
 
0.9%
139 33
 
0.6%
150 30
 
0.6%
249 26
 
0.5%
79 21
 
0.4%
Other values (37) 150
 
2.8%
(Missing) 4295
81.5%
ValueCountFrequency (%)
5 7
 
0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
19 2
 
< 0.1%
25 49
 
0.9%
29 15
 
0.3%
30 1
 
< 0.1%
39 13
 
0.2%
40 2
 
< 0.1%
49 270
5.1%
ValueCountFrequency (%)
39960 1
 
< 0.1%
29970 2
 
< 0.1%
4999 4
0.1%
4990 1
 
< 0.1%
450 1
 
< 0.1%
399 2
 
< 0.1%
375 1
 
< 0.1%
350 3
 
0.1%
300 3
 
0.1%
299 9
0.2%
Distinct932
Distinct (%)99.8%
Missing4335
Missing (%)82.3%
Memory size41.3 KiB
2023-06-15T08:21:40.421748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10163
Median length1365
Mean length1210.4839
Min length137

Characters and Unicode

Total characters1130592
Distinct characters1113
Distinct categories21 ?
Distinct scripts7 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique931 ?
Unique (%)99.7%

Sample

1st rowDesigned for those who are new to elearning, this course will prepare you with strategies to be a successful online learner.The edX learning design team has curated some of the most powerful, science-backed techniques which you can start using right away and on any learning platform.The Verified Certificate for this course is free. Use the following coupon code before September 1, 2020 to upgrade at no cost to you: Y5ZADM5NU2AN5JU7This course will help you answer the following questions:
2nd rowThis course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook "Python for Everybody". Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
3rd rowThis is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course. Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.
4th rowIn the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a "quick question" to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we'll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments.
5th rowBegin your journey in a new career in marketing analytics. Learn about powerful strategies and methodology, starting with identifying market trends and metrics used to measure marketing success.In this marketing course, you will learn how to execute market sizing, identify market trends, and predict future conditions.This course is taught by Stephan Sorger who has held leadership roles in marketing and product development at companies such as Oracle, 3Com and NASA. He has also taught for over a decade at UC Berkeley Extension and is the author of two widely adopted marketing textbooks. This course will equip you with the knowledge and skills necessary to immediately see practical benefits in the workplace.Analytics-based marketing is increasingly important in determining a company’s spending and ROI. Many entry-level positions in marketing now require some basic level of knowledge in this rapidly growing field.
ValueCountFrequency (%)
the 7102
 
4.1%
and 6425
 
3.8%
of 4585
 
2.7%
to 4378
 
2.6%
a 3053
 
1.8%
in 2779
 
1.6%
de 2645
 
1.5%
will 2178
 
1.3%
you 2176
 
1.3%
course 2159
 
1.3%
Other values (19110) 133715
78.1%
2023-06-15T08:21:42.365745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170244
15.1%
e 108751
 
9.6%
a 76963
 
6.8%
o 73361
 
6.5%
i 70503
 
6.2%
t 69170
 
6.1%
n 68807
 
6.1%
s 66279
 
5.9%
r 59858
 
5.3%
l 42492
 
3.8%
Other values (1103) 324164
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 905179
80.1%
Space Separator 170251
 
15.1%
Uppercase Letter 22115
 
2.0%
Other Punctuation 20619
 
1.8%
Other Letter 5323
 
0.5%
Decimal Number 2381
 
0.2%
Dash Punctuation 2179
 
0.2%
Final Punctuation 749
 
0.1%
Close Punctuation 671
 
0.1%
Open Punctuation 659
 
0.1%
Other values (11) 466
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
237
 
4.5%
109
 
2.0%
53
 
1.0%
52
 
1.0%
52
 
1.0%
50
 
0.9%
49
 
0.9%
49
 
0.9%
45
 
0.8%
44
 
0.8%
Other values (942) 4583
86.1%
Lowercase Letter
ValueCountFrequency (%)
e 108751
12.0%
a 76963
 
8.5%
o 73361
 
8.1%
i 70503
 
7.8%
t 69170
 
7.6%
n 68807
 
7.6%
s 66279
 
7.3%
r 59858
 
6.6%
l 42492
 
4.7%
c 38989
 
4.3%
Other values (39) 230006
25.4%
Uppercase Letter
ValueCountFrequency (%)
T 2744
 
12.4%
I 1762
 
8.0%
A 1610
 
7.3%
S 1552
 
7.0%
C 1508
 
6.8%
M 1378
 
6.2%
E 1316
 
6.0%
P 1250
 
5.7%
D 1032
 
4.7%
W 921
 
4.2%
Other values (22) 7042
31.8%
Other Punctuation
ValueCountFrequency (%)
, 9646
46.8%
. 7436
36.1%
: 769
 
3.7%
? 546
 
2.6%
' 486
 
2.4%
" 257
 
1.2%
; 226
 
1.1%
/ 218
 
1.1%
185
 
0.9%
! 151
 
0.7%
Other values (16) 699
 
3.4%
Decimal Number
ValueCountFrequency (%)
0 559
23.5%
1 478
20.1%
2 421
17.7%
3 201
 
8.4%
5 179
 
7.5%
4 127
 
5.3%
9 114
 
4.8%
8 113
 
4.7%
7 101
 
4.2%
6 88
 
3.7%
Close Punctuation
ValueCountFrequency (%)
) 646
96.3%
13
 
1.9%
9
 
1.3%
} 1
 
0.1%
1
 
0.1%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 634
96.2%
13
 
2.0%
9
 
1.4%
{ 1
 
0.2%
1
 
0.2%
[ 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 1975
90.6%
105
 
4.8%
96
 
4.4%
2
 
0.1%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 9
56.2%
~ 4
25.0%
= 2
 
12.5%
< 1
 
6.2%
Final Punctuation
ValueCountFrequency (%)
621
82.9%
121
 
16.2%
» 7
 
0.9%
Initial Punctuation
ValueCountFrequency (%)
124
75.6%
33
 
20.1%
« 7
 
4.3%
Other Symbol
ValueCountFrequency (%)
® 48
54.5%
36
40.9%
4
 
4.5%
Space Separator
ValueCountFrequency (%)
170244
> 99.9%
  7
 
< 0.1%
Control
ValueCountFrequency (%)
144
98.6%
2
 
1.4%
Currency Symbol
ValueCountFrequency (%)
$ 21
95.5%
1
 
4.5%
Modifier Letter
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
50.0%
^ 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%
Private Use
ValueCountFrequency (%)
6
100.0%
Nonspacing Mark
ValueCountFrequency (%)
́ 3
100.0%
Format
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 927294
82.0%
Common 197965
 
17.5%
Han 4993
 
0.4%
Hiragana 313
 
< 0.1%
Katakana 18
 
< 0.1%
Unknown 6
 
< 0.1%
Inherited 3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
237
 
4.7%
109
 
2.2%
53
 
1.1%
52
 
1.0%
52
 
1.0%
50
 
1.0%
49
 
1.0%
49
 
1.0%
45
 
0.9%
44
 
0.9%
Other values (888) 4253
85.2%
Latin
ValueCountFrequency (%)
e 108751
11.7%
a 76963
 
8.3%
o 73361
 
7.9%
i 70503
 
7.6%
t 69170
 
7.5%
n 68807
 
7.4%
s 66279
 
7.1%
r 59858
 
6.5%
l 42492
 
4.6%
c 38989
 
4.2%
Other values (71) 252121
27.2%
Common
ValueCountFrequency (%)
170244
86.0%
, 9646
 
4.9%
. 7436
 
3.8%
- 1975
 
1.0%
: 769
 
0.4%
) 646
 
0.3%
( 634
 
0.3%
621
 
0.3%
0 559
 
0.3%
? 546
 
0.3%
Other values (67) 4889
 
2.5%
Hiragana
ValueCountFrequency (%)
20
 
6.4%
20
 
6.4%
20
 
6.4%
20
 
6.4%
16
 
5.1%
16
 
5.1%
12
 
3.8%
12
 
3.8%
12
 
3.8%
12
 
3.8%
Other values (35) 153
48.9%
Katakana
ValueCountFrequency (%)
4
22.2%
3
16.7%
3
16.7%
2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Unknown
ValueCountFrequency (%)
6
100.0%
Inherited
ValueCountFrequency (%)
́ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1119236
99.0%
CJK 4992
 
0.4%
None 4745
 
0.4%
Punctuation 1219
 
0.1%
Hiragana 313
 
< 0.1%
Katakana 37
 
< 0.1%
Geometric Shapes 36
 
< 0.1%
PUA 6
 
< 0.1%
Letterlike Symbols 4
 
< 0.1%
Diacriticals 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
170244
15.2%
e 108751
 
9.7%
a 76963
 
6.9%
o 73361
 
6.6%
i 70503
 
6.3%
t 69170
 
6.2%
n 68807
 
6.1%
s 66279
 
5.9%
r 59858
 
5.3%
l 42492
 
3.8%
Other values (84) 312808
27.9%
None
ValueCountFrequency (%)
á 1137
24.0%
ó 1133
23.9%
é 648
13.7%
í 605
12.8%
185
 
3.9%
ñ 159
 
3.4%
ú 149
 
3.1%
117
 
2.5%
¿ 97
 
2.0%
89
 
1.9%
Other values (39) 426
 
9.0%
Punctuation
ValueCountFrequency (%)
621
50.9%
124
 
10.2%
121
 
9.9%
105
 
8.6%
100
 
8.2%
96
 
7.9%
33
 
2.7%
14
 
1.1%
2
 
0.2%
2
 
0.2%
CJK
ValueCountFrequency (%)
237
 
4.7%
109
 
2.2%
53
 
1.1%
52
 
1.0%
52
 
1.0%
50
 
1.0%
49
 
1.0%
49
 
1.0%
45
 
0.9%
44
 
0.9%
Other values (887) 4252
85.2%
Geometric Shapes
ValueCountFrequency (%)
36
100.0%
Hiragana
ValueCountFrequency (%)
20
 
6.4%
20
 
6.4%
20
 
6.4%
20
 
6.4%
16
 
5.1%
16
 
5.1%
12
 
3.8%
12
 
3.8%
12
 
3.8%
12
 
3.8%
Other values (35) 153
48.9%
Katakana
ValueCountFrequency (%)
16
43.2%
4
 
10.8%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
1
 
2.7%
1
 
2.7%
1
 
2.7%
1
 
2.7%
Other values (2) 2
 
5.4%
PUA
ValueCountFrequency (%)
6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%
Diacriticals
ValueCountFrequency (%)
́ 3
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
Distinct412
Distinct (%)98.8%
Missing4852
Missing (%)92.1%
Memory size41.3 KiB
2023-06-15T08:21:43.085738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5057
Median length886
Mean length877.70024
Min length4

Characters and Unicode

Total characters366001
Distinct characters366
Distinct categories19 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique409 ?
Unique (%)98.1%

Sample

1st rowWelcome - We start with opportunities to meet your instructors and fellow learners. Self-care for Learning - In this module, we then explore baseline self-care strategies that will help you maintain a healthy mind for effective online learning, the connections between memory and learning, and the importance of sleep. Space, Time, and Technology - In this module we address the challenges involved with creating a space for learning, including managing your technology. We also cover techniques for time management and keeping a routine. Learning Strategies - This module will help you get the most out your online learning experience. We cover effective study strategies and practices, making plans and setting priorities, and practicing self-regulation skills. Communication and Community - In this module, we talk about the importance of social learning. We cover strategies for communication, collaborating, and building connections with your instructors and fellow learners. What's Next? - Get started learning online!
2nd rowMODULE 1: INTRODUCTION TO TEAMS Focuses on recognizing the distinction between groups and teams; developing an understanding of your own group/team loyalties and priorities and considering the building blocks for high-performing teams. MODULE 2: MOTIVATING AND ENGAGING PEOPLE Examines what motivates and engages people at work, developing strategies for improving motivation and engagement in your employees, and what motivates and drives your own behavior. MODULE 3: MANAGING WORK RELATIONSHIPS Considers the nature of your work relationships, how to develop strategies for strengthening employee trust and attachment to the group, how to manage those particularly difficult people at work, and recognizing the importance of external stakeholder relationships. MODULE 4: LEADING TEAMS FOR EXECUTION Considers how to recognize the ingredients for team execution, how to identify challenges in team communication and coordination, and how to develop strategies to enhance team communication/coordination. MODULE 5: LEADING TEAMS TO SOLVE PROBLEMS Focuses on recognizing the value of openness and inclusion for problem solving and creative teams, how to develop better problem solving in your teams - both in execution and in team culture. MODULE 6: WHEN GOOD TEAMS FAIL (PART 1): TOO MUCH CONFLICT! Examines the causes and consequences of serious and escalating conflict, developing strategies for preventing serious/escalating conflict, and developing competencies for resolving serious conflict. MODULE 7: WHEN GOOD TEAMS FAIL (PART 2): TOO MUCH COHESION! Enables you to recognize the warning signs that your team is too cohesive and develop strategies for promoting productive conflict in teams. MODULE 8: BRINGING DIVIDED GROUPS TOGETHER Enables you to recognize patterns and implications of intergroup behavior in your organization, develop strategies for bridging organizational silos and identify the steps for building an inclusive organizational identity. MODULE 9: ORGANIZATIONAL CULTURE Enables you to identify the impact of organizational culture and its (non) alignment to a broader organizational strategy, and recognize points of potential influence when trying to change organizational culture. MODULE 10: BRINGING IT TOGETHER: ANALYZING AND DEVELOPING YOUR TEAM Develops skills in recognizing the strengths and weaknesses of your team, critically analyzing the processes affecting team effectiveness, and considering ways to further develop your team for high-performance.
3rd rowModule 1: Mental fitnessBy the end of this module, you will be able to:Module 2: Cognitive-behavioural strategies to increase mental fitnessBy the end of this module, you will be able to:
4th rowWeek 1: Six Sigma Introduction Introduction to the Six Sigma Methodology and the DMAIC process improvement cycle. Understand the contributors to the cost of quality. Discuss the difference between defects and defectives in a process and how to calculate process yield, including a comparison of processes of different complexity using the metric DPMO.Week 2: DEFINE - Defining the Problem Discuss how to understand customer expectations, using the Kano Model to categorize quality characteristics. Start the first and difficult task of a Six Sigma project, Defining the Problem, and review the key content in a Project Charter.Week 3: MEASURE - Statistics Review Review of random variables and probability distributions used commonly in quality engineering, such as Binomial, Poisson, and Exponential. Cover descriptive statistics, emphasizing the importance of clearly communicating the results of your project.Week 4: MEASURE - Normal Distribution Learn the characteristics of the Normal Distribution and how to use the Standard Normal to calculate probabilities related to normally distributed variables. Cover the Central Limit Theorem, and how it relates to sampling theory.Week 5: MEASURE - Process Mapping Introduce Process Mapping, including SIPOC and Value Stream Mapping. We identify the Critical-to-Quality characteristic for a Six Sigma projectWeek 6: MEASURE - Measurement System Analysis Learn the basics of Measurement Theory and Sampling Plans, including Precision, Accuracy, Linearity, Bias, Stability, Gage Repeatability & ReproducibilityWeek 7: MEASURE - Process Capability Introduction to Process Capability and the metrics CP/CPK for establishing our baseline process performance.Week 8: Quality Topics and Course Summary Cover the basics of Tolerance Design and the risk assessment tool failure Mode and Effects Analysis (FMEA). Review the complete Six Sigma Roadmap before summarizing and closing the course.
5th row1 Basic Counting2 Advanced Counting3 Basic Probability4 Expected Value5 Conditional Probability6 Bernoulli Trials7 The Normal Distribution
ValueCountFrequency (%)
and 2262
 
4.1%
the 2092
 
3.8%
of 1444
 
2.6%
to 1075
 
2.0%
de 879
 
1.6%
in 708
 
1.3%
a 654
 
1.2%
598
 
1.1%
week 578
 
1.1%
module 519
 
0.9%
Other values (10621) 44061
80.3%
2023-06-15T08:21:44.220469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53080
14.5%
e 33703
 
9.2%
a 23836
 
6.5%
i 22876
 
6.3%
n 22687
 
6.2%
o 22616
 
6.2%
t 21655
 
5.9%
s 19038
 
5.2%
r 16642
 
4.5%
l 13008
 
3.6%
Other values (356) 116860
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 276499
75.5%
Space Separator 53081
 
14.5%
Uppercase Letter 18516
 
5.1%
Other Punctuation 8771
 
2.4%
Decimal Number 4335
 
1.2%
Control 1601
 
0.4%
Dash Punctuation 1104
 
0.3%
Other Letter 1013
 
0.3%
Close Punctuation 372
 
0.1%
Open Punctuation 362
 
0.1%
Other values (9) 347
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 115
 
11.4%
ل 84
 
8.3%
م 48
 
4.7%
ة 33
 
3.3%
ت 33
 
3.3%
و 33
 
3.3%
ي 32
 
3.2%
ن 27
 
2.7%
ر 24
 
2.4%
ع 23
 
2.3%
Other values (221) 561
55.4%
Lowercase Letter
ValueCountFrequency (%)
e 33703
12.2%
a 23836
 
8.6%
i 22876
 
8.3%
n 22687
 
8.2%
o 22616
 
8.2%
t 21655
 
7.8%
s 19038
 
6.9%
r 16642
 
6.0%
l 13008
 
4.7%
c 11996
 
4.3%
Other values (29) 68442
24.8%
Uppercase Letter
ValueCountFrequency (%)
M 1610
 
8.7%
T 1505
 
8.1%
S 1415
 
7.6%
C 1400
 
7.6%
I 1365
 
7.4%
W 1296
 
7.0%
E 1206
 
6.5%
P 1104
 
6.0%
A 1075
 
5.8%
D 1018
 
5.5%
Other values (22) 5522
29.8%
Other Punctuation
ValueCountFrequency (%)
. 3186
36.3%
: 2294
26.2%
, 2184
24.9%
? 264
 
3.0%
; 138
 
1.6%
* 129
 
1.5%
' 120
 
1.4%
¿ 81
 
0.9%
/ 80
 
0.9%
& 77
 
0.9%
Other values (15) 218
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 1018
23.5%
2 826
19.1%
3 680
15.7%
4 563
13.0%
5 385
 
8.9%
6 260
 
6.0%
0 218
 
5.0%
7 162
 
3.7%
8 132
 
3.0%
9 91
 
2.1%
Nonspacing Mark
ValueCountFrequency (%)
ّ 7
58.3%
ُ 3
25.0%
َ 1
 
8.3%
ً 1
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 969
87.8%
105
 
9.5%
30
 
2.7%
Close Punctuation
ValueCountFrequency (%)
) 351
94.4%
18
 
4.8%
] 3
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 341
94.2%
18
 
5.0%
[ 3
 
0.8%
Math Symbol
ValueCountFrequency (%)
| 6
50.0%
+ 4
33.3%
2
 
16.7%
Space Separator
ValueCountFrequency (%)
53080
> 99.9%
  1
 
< 0.1%
Control
ValueCountFrequency (%)
1585
99.0%
16
 
1.0%
Final Punctuation
ValueCountFrequency (%)
133
70.7%
55
29.3%
Initial Punctuation
ValueCountFrequency (%)
55
96.5%
2
 
3.5%
Connector Punctuation
ValueCountFrequency (%)
_ 40
100.0%
Other Symbol
ValueCountFrequency (%)
30
100.0%
Format
ValueCountFrequency (%)
4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 295019
80.6%
Common 69961
 
19.1%
Arabic 643
 
0.2%
Han 366
 
0.1%
Inherited 12
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
12
 
3.3%
10
 
2.7%
8
 
2.2%
8
 
2.2%
7
 
1.9%
7
 
1.9%
6
 
1.6%
5
 
1.4%
5
 
1.4%
5
 
1.4%
Other values (186) 293
80.1%
Latin
ValueCountFrequency (%)
e 33703
 
11.4%
a 23836
 
8.1%
i 22876
 
7.8%
n 22687
 
7.7%
o 22616
 
7.7%
t 21655
 
7.3%
s 19038
 
6.5%
r 16642
 
5.6%
l 13008
 
4.4%
c 11996
 
4.1%
Other values (62) 86962
29.5%
Common
ValueCountFrequency (%)
53080
75.9%
. 3186
 
4.6%
: 2294
 
3.3%
, 2184
 
3.1%
1585
 
2.3%
1 1018
 
1.5%
- 969
 
1.4%
2 826
 
1.2%
3 680
 
1.0%
4 563
 
0.8%
Other values (50) 3576
 
5.1%
Arabic
ValueCountFrequency (%)
ا 115
17.9%
ل 84
13.1%
م 48
 
7.5%
ة 33
 
5.1%
ت 33
 
5.1%
و 33
 
5.1%
ي 32
 
5.0%
ن 27
 
4.2%
ر 24
 
3.7%
ع 23
 
3.6%
Other values (24) 191
29.7%
Inherited
ValueCountFrequency (%)
ّ 7
58.3%
ُ 3
25.0%
َ 1
 
8.3%
ً 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362653
99.1%
None 1833
 
0.5%
Arabic 669
 
0.2%
Punctuation 448
 
0.1%
CJK 366
 
0.1%
Geometric Shapes 30
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53080
14.6%
e 33703
 
9.3%
a 23836
 
6.6%
i 22876
 
6.3%
n 22687
 
6.3%
o 22616
 
6.2%
t 21655
 
6.0%
s 19038
 
5.2%
r 16642
 
4.6%
l 13008
 
3.6%
Other values (78) 113512
31.3%
None
ValueCountFrequency (%)
ó 796
43.4%
á 271
 
14.8%
é 222
 
12.1%
í 197
 
10.7%
ñ 85
 
4.6%
¿ 81
 
4.4%
ú 25
 
1.4%
§ 22
 
1.2%
18
 
1.0%
18
 
1.0%
Other values (21) 98
 
5.3%
Punctuation
ValueCountFrequency (%)
133
29.7%
105
23.4%
63
14.1%
55
12.3%
55
12.3%
30
 
6.7%
4
 
0.9%
2
 
0.4%
1
 
0.2%
Arabic
ValueCountFrequency (%)
ا 115
17.2%
ل 84
 
12.6%
م 48
 
7.2%
ة 33
 
4.9%
ت 33
 
4.9%
و 33
 
4.9%
ي 32
 
4.8%
ن 27
 
4.0%
ر 24
 
3.6%
ع 23
 
3.4%
Other values (30) 217
32.4%
Geometric Shapes
ValueCountFrequency (%)
30
100.0%
CJK
ValueCountFrequency (%)
12
 
3.3%
10
 
2.7%
8
 
2.2%
8
 
2.2%
7
 
1.9%
7
 
1.9%
6
 
1.6%
5
 
1.4%
5
 
1.4%
5
 
1.4%
Other values (186) 293
80.1%
Math Operators
ValueCountFrequency (%)
2
100.0%

paid
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing623
Missing (%)11.8%
Memory size41.3 KiB
True
3362 
False
1284 
(Missing)
623 
ValueCountFrequency (%)
True 3362
63.8%
False 1284
 
24.4%
(Missing) 623
 
11.8%
2023-06-15T08:21:44.604435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

n_reviews
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct511
Distinct (%)13.9%
Missing1597
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean156.37146
Minimum0
Maximum27445
Zeros284
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:44.908436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median18
Q367
95-th percentile485.15
Maximum27445
Range27445
Interquartile range (IQR)63

Descriptive statistics

Standard deviation936.17865
Coefficient of variation (CV)5.9868895
Kurtosis398.54493
Mean156.37146
Median Absolute Deviation (MAD)16
Skewness17.803799
Sum574196
Variance876430.46
MonotonicityNot monotonic
2023-06-15T08:21:45.284438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284
 
5.4%
1 184
 
3.5%
2 166
 
3.2%
3 160
 
3.0%
4 127
 
2.4%
5 109
 
2.1%
6 101
 
1.9%
8 82
 
1.6%
10 78
 
1.5%
11 76
 
1.4%
Other values (501) 2305
43.7%
(Missing) 1597
30.3%
ValueCountFrequency (%)
0 284
5.4%
1 184
3.5%
2 166
3.2%
3 160
3.0%
4 127
2.4%
5 109
 
2.1%
6 101
 
1.9%
7 73
 
1.4%
8 82
 
1.6%
9 71
 
1.3%
ValueCountFrequency (%)
27445 1
< 0.1%
22412 1
< 0.1%
19649 1
< 0.1%
16976 1
< 0.1%
15117 1
< 0.1%
11580 1
< 0.1%
11123 1
< 0.1%
8629 1
< 0.1%
8341 1
< 0.1%
7676 1
< 0.1%

n_lectures
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct229
Distinct (%)6.2%
Missing1597
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean40.140251
Minimum0
Maximum779
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2023-06-15T08:21:45.686124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median25
Q346
95-th percentile119
Maximum779
Range779
Interquartile range (IQR)31

Descriptive statistics

Standard deviation50.417102
Coefficient of variation (CV)1.2560236
Kurtosis36.744899
Mean40.140251
Median Absolute Deviation (MAD)13
Skewness4.8701263
Sum147395
Variance2541.8842
MonotonicityNot monotonic
2023-06-15T08:21:46.094103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 121
 
2.3%
15 109
 
2.1%
13 107
 
2.0%
14 105
 
2.0%
11 104
 
2.0%
16 99
 
1.9%
9 99
 
1.9%
20 94
 
1.8%
19 90
 
1.7%
24 88
 
1.7%
Other values (219) 2656
50.4%
(Missing) 1597
30.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
4 2
 
< 0.1%
5 54
1.0%
6 63
1.2%
7 77
1.5%
8 83
1.6%
9 99
1.9%
10 87
1.7%
11 104
2.0%
12 121
2.3%
ValueCountFrequency (%)
779 1
< 0.1%
544 1
< 0.1%
536 1
< 0.1%
527 1
< 0.1%
491 1
< 0.1%
462 1
< 0.1%
460 1
< 0.1%
458 1
< 0.1%
454 1
< 0.1%
444 1
< 0.1%
Distinct3672
Distinct (%)100.0%
Missing1597
Missing (%)30.3%
Memory size41.3 KiB
Minimum2011-07-09 05:43:31
Maximum2017-07-06 21:46:30
2023-06-15T08:21:46.526103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:46.931616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-06-15T08:21:16.688233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:11.323124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:13.013865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:15.124197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:17.064206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:11.709521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:13.676198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:15.470628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:17.371524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:12.253523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:14.532247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:15.942599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:17.747522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:12.629865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:14.828220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-15T08:21:16.287805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-15T08:21:47.397605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
n_subscriberspricen_reviewsn_lecturesmoocmodalitylevelsubjectlanguagesubtitlespaid
n_subscribers1.0000.1220.7840.2100.1690.0000.0830.1580.0000.0000.143
price0.1221.000NaNNaN1.0000.0000.0840.0000.0000.0001.000
n_reviews0.784NaN1.0000.3411.0000.0000.0000.0410.0000.0000.079
n_lectures0.210NaN0.3411.0001.0000.0000.0610.0940.0000.0000.074
mooc0.1691.0001.0001.0001.0001.0000.9990.9961.0001.0000.833
modality0.0000.0000.0000.0001.0001.0000.3610.0800.1860.1141.000
level0.0830.0840.0000.0610.9990.3611.0000.4430.0920.1090.835
subject0.1580.0000.0410.0940.9960.0800.4431.0000.1140.2240.830
language0.0000.0000.0000.0001.0000.1860.0920.1141.0000.9371.000
subtitles0.0000.0000.0000.0001.0000.1140.1090.2240.9371.0001.000
paid0.1431.0000.0790.0740.8331.0000.8350.8301.0001.0001.000

Missing values

2023-06-15T08:21:18.371808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-15T08:21:19.438206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-15T08:21:20.454502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

titleinstitutionurlidmoocsummaryn_subscribersmodalityinstructorslevelsubjectlanguagesubtitleseffortdurationpricedescriptioncurriculumpaidn_reviewsn_lecturespublished
0Machine LearningStanford Universityhttps://www.coursera.org/learn/machine-learningmachine-learningcourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Indigenous CanadaUniversity of Albertahttps://www.coursera.org/learn/indigenous-canadaindigenous-canadacourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2The Science of Well-BeingYale Universityhttps://www.coursera.org/learn/the-science-of-well-beingthe-science-of-well-beingcourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3Technical Support FundamentalsGooglehttps://www.coursera.org/learn/technical-support-fundamentalstechnical-support-fundamentalscourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4Become a CBRS Certified Professional Installer by GoogleGoogle - Spectrum Sharinghttps://www.coursera.org/learn/google-cbrs-cpi-traininggoogle-cbrs-cpi-trainingcourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5Financial MarketsYale Universityhttps://www.coursera.org/learn/financial-markets-globalfinancial-markets-globalcourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6Introduction to PsychologyYale Universityhttps://www.coursera.org/learn/introduction-psychologyintroduction-psychologycourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7Programming for Everybody (Getting Started with Python)University of Michiganhttps://www.coursera.org/learn/pythonpythoncourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8The Bits and Bytes of Computer NetworkingGooglehttps://www.coursera.org/learn/computer-networkingcomputer-networkingcourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9AI For EveryoneDeepLearning.AIhttps://www.coursera.org/learn/ai-for-everyoneai-for-everyonecourseraNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
titleinstitutionurlidmoocsummaryn_subscribersmodalityinstructorslevelsubjectlanguagesubtitleseffortdurationpricedescriptioncurriculumpaidn_reviewsn_lecturespublished
5259A how to guide in HTMLNaNhttps://www.udemy.com/a-how-to-guide-in-html/270976udemyNaN7318.0NaNNaNBeginner LevelWeb DevelopmentNaNNaNNaN0.5833333333333334NaNNaNNaNFalse205.08.02014-08-10T20:19:10Z
5260Building Better APIs with GraphQLNaNhttps://www.udemy.com/building-better-apis-with-graphql/679992udemyNaN555.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN2.5NaNNaNNaNTrue89.016.02015-11-29T22:02:02Z
5261Learn Grunt with Examples: Automate Your Front End WorkflowNaNhttps://www.udemy.com/learn-grunt-automate-your-front-end-workflow/330900udemyNaN496.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN1.0NaNNaNNaNTrue113.017.02014-12-19T21:38:54Z
5262Build A Stock Downloader With Visual Studio 2015 And C#NaNhttps://www.udemy.com/csharpyahoostockdownloader/667122udemyNaN436.0NaNNaNIntermediate LevelWeb DevelopmentNaNNaNNaN1.5NaNNaNNaNTrue36.022.02015-11-19T17:22:47Z
5263jQuery UI in Action: Build 5 jQuery UI ProjectsNaNhttps://www.udemy.com/jquery-ui-practical-build-jquery-ui-projects/865438udemyNaN382.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN15.5NaNNaNNaNTrue28.0140.02016-10-10T22:00:32Z
5264Learn jQuery from Scratch - Master of JavaScript libraryNaNhttps://www.udemy.com/easy-jquery-for-beginner-learn-from-scratch-step-by-step/775618udemyNaN1040.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN2.0NaNNaNNaNTrue14.021.02016-06-14T17:36:46Z
5265How To Design A WordPress Website With No Coding At AllNaNhttps://www.udemy.com/how-to-make-a-wordpress-website-course/1088178udemyNaN306.0NaNNaNBeginner LevelWeb DevelopmentNaNNaNNaN3.5NaNNaNNaNTrue3.042.02017-03-10T22:24:30Z
5266Learn and Build using PolymerNaNhttps://www.udemy.com/learn-and-build-using-polymer/635248udemyNaN513.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN3.5NaNNaNNaNTrue169.048.02015-12-30T16:41:42Z
5267CSS Animations: Create Amazing Effects on Your WebsiteNaNhttps://www.udemy.com/css-animations-create-amazing-effects-on-your-website/905096udemyNaN300.0NaNNaNAll LevelsWeb DevelopmentNaNNaNNaN3.0NaNNaNNaNTrue31.038.02016-08-11T19:06:15Z
5268Using MODX CMS to Build Websites: A Beginner's GuideNaNhttps://www.udemy.com/using-modx-cms-to-build-websites-a-beginners-guide/297602udemyNaN901.0NaNNaNBeginner LevelWeb DevelopmentNaNNaNNaN2.0NaNNaNNaNTrue36.020.02014-09-28T19:51:11Z